In [ ]:
from PIL import Image, ImageEnhance
import numpy as np
import matplotlib.pyplot as plt
import cv2
import os
import pandas as pd
import math
import requests
import json
import re
import csv
directory_path = os.getcwd()
parent_directory_path = os.path.dirname(directory_path)
csv_path = os.path.join(parent_directory_path, 'Model\\condo_data_new_FINAL_test.csv')
gt_masked_image_path = os.path.join(parent_directory_path, 'Model\\clear\\test')
generated_image_path = os.path.join(parent_directory_path, 'Model\\clear\\final_clear_output_2')
# Read the CSV file
data = pd.read_csv(csv_path)
# Function to extract the numeric part of the filename
def extract_numeric_part(filename):
numeric_part = ''.join(filter(str.isdigit, filename))
return int(numeric_part) if numeric_part else None
def create_binary_mask(arr, target_color, threshold=30):
lower_bound = np.array(target_color) - threshold
upper_bound = np.array(target_color) + threshold
mask = (arr[:, :, :3] >= lower_bound) & (arr[:, :, :3] <= upper_bound)
return np.all(mask, axis=-1)
def extract_building_regions(arr, target_color, threshold=10):
lower_bound = np.array(target_color) - threshold
upper_bound = np.array(target_color) + threshold
mask = (arr[:, :, :3] >= lower_bound) & (arr[:, :, :3] <= upper_bound)
return np.all(mask, axis=-1)
# def find_max_building_storeys(gpr):
# max_building_storeys= 0
# if gpr >= 0 and gpr < 1.4:
# max_building_storeys = 5
# elif gpr >= 1.4 and gpr < 1.6:
# max_building_storeys = 12
# elif gpr >= 1.6 and gpr < 2.1:
# max_building_storeys = 24
# elif gpr >= 2.1 and gpr < 2.8:
# max_building_storeys = 36
# elif gpr >= 2.8:
# max_building_storeys = 48 ## by right got no limit
# return max_building_storeys
def masked_rgb(simp_gpr):
rgb = [0,0,0]
if simp_gpr == 1.4:
rgb = [0,255,0]
elif simp_gpr == 1.6:
rgb = [200,130,60]
elif simp_gpr == 2.1:
rgb = [255,255,0]
elif simp_gpr == 2.8:
rgb = [255,0,0]
elif simp_gpr == 3.0:
rgb =[0,0,255]
return rgb
'''
pink, [255, 10, 169]
brown, [200,130,60]
cyan, [0,255,255]
red, [255,0,0]
black, [0,0,0]
green, [0,255,0]
blue, [0,0,255]
yellow, [255, 255, 0]
'''
# absolute_accuracies = []
# losses =[]
# images =[]
# sanity_ratios =[]
gprs =[]
generated_gprs =[]
sanity_ratios =[]
# Iterate through the images in the generated_image_path
for image_file in os.listdir(generated_image_path):
if image_file.endswith('.png'):
image_index = extract_numeric_part(image_file)
# Construct the path for the corresponding masked image
gt_mask_image_filename = f"{image_index}.png"
gt_mask_image = os.path.join(gt_masked_image_path, gt_mask_image_filename)
open_gt_mask_image = Image.open(gt_mask_image)
mask_crop_box = (512, 0, 1024, 512) # right side
mask_image = open_gt_mask_image.crop(mask_crop_box) #gt_mask is concatenated gt and mask
gt_crop_box = (0, 0, 512, 512) # left side
gt_image = open_gt_mask_image.crop(gt_crop_box)
generated_image = os.path.join(generated_image_path, image_file)
generated_image = Image.open(generated_image)
# Check if the image index matches any index in the CSV
matched_row = data[data['key1'] == image_index]
if not matched_row.empty:
# Extract the GPR value for the matched row
gpr_value = matched_row['GPR'].iloc[0]
storey = matched_row['storeys'].iloc[0]
simplified_gpr_value = matched_row['simp_gpr'].iloc[0]
actual_site_area = matched_row['area'].iloc[0]
actual_site_area = actual_site_area.replace(',', '')
actual_site_area = float(actual_site_area[:-4])
gpr_value = float(gpr_value)
storey = int(storey)
mask_array = np.array(mask_image)
generated_array = np.array(generated_image)
mask_color = masked_rgb(simplified_gpr_value)
site_mask = create_binary_mask(mask_array, mask_color)
site_area_array = generated_array.copy()
site_area_array[~site_mask] = [255, 255, 255, 255] # making non-masked region white RMB ITS 4 CHANNELS NOW
site_area_image = Image.fromarray(site_area_array)
mask_color = [255, 10, 169] # pink
building_mask = extract_building_regions(site_area_array, mask_color)
buildings_image = Image.fromarray(building_mask)
plt.figure(figsize=(20, 5))
plt.subplot(1, 4, 1)
plt.imshow(mask_image)
plt.title('Mask Image')
plt.axis('off')
plt.subplot(1, 4, 2)
plt.imshow(gt_image)
plt.title('GT Image')
plt.axis('off')
plt.subplot(1, 4, 3)
plt.imshow(generated_image)
plt.title('Generated Image')
plt.axis('off')
plt.subplot(1, 4, 4)
plt.imshow(buildings_image, cmap='gray')
plt.title('Buildings Image')
plt.axis('off')
plt.show()
# accuracy
building_pixels = np.sum(building_mask)
mask_pixels = np.sum(site_mask)
msq_per_pixel = actual_site_area/mask_pixels
building_area = msq_per_pixel * building_pixels
#max_storeys = find_max_building_storeys(gpr_value)
generated_gpr = building_area*storey/actual_site_area
gprs.append(gpr_value)
generated_gprs.append(generated_gpr)
# if generated_gpr == 0:
# accuracy = 0
# else:
# accuracy = (gpr_value - generated_gpr) / gpr_value #gpr_value is the target gpr
# loss =
# images.append(image_file)
# absolute_accuracy = abs(accuracy)
# absolute_accuracies.append(absolute_accuracy)
print(f'Image: {image_file}, GPR: {gpr_value}, Simplified GPR: {simplified_gpr_value}, Storeys:{storey}, Site area: {actual_site_area}, Building pixels: {building_pixels}, Mask pixels: {mask_pixels}, Generated GPR: {generated_gpr}')
#sanity check. ratios should be about 0.75
ratio = mask_pixels/actual_site_area
sanity_ratios.append(ratio)
total_data = len(gprs)
accuracies = []
absolute_error =[]
square_error =[]
for tar_gpr, gen_gpr in zip(gprs, generated_gprs):
accuracies.append(abs((tar_gpr-gen_gpr)/tar_gpr))
absolute_error.append(abs(tar_gpr-gen_gpr))
square_error.append((tar_gpr-gen_gpr)**2)
accuracy = sum(accuracies)/total_data
mean_abs_error = sum(absolute_error)/total_data
root_squared_error = math.sqrt(sum(square_error)/total_data)
print(f"Accuracies:{accuracies} \nSquare error:{square_error} \nAbsolute error:{absolute_error} ")
print(f"\nAccuracy:{accuracy} MAE:{mean_abs_error} RMSE:{root_squared_error}")
Image: 1040.png, GPR: 1.4, Simplified GPR: 1.4, Storeys:5, Site area: 23065.1, Building pixels: 5727, Mask pixels: 15996, Generated GPR: 1.7901350337584396
Image: 1074.png, GPR: 2.5, Simplified GPR: 2.8, Storeys:12, Site area: 37265.0, Building pixels: 4835, Mask pixels: 27225, Generated GPR: 2.131129476584022
Image: 1076.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:36, Site area: 10414.2, Building pixels: 2099, Mask pixels: 8425, Generated GPR: 8.969020771513353
Image: 1102.png, GPR: 1.6, Simplified GPR: 1.6, Storeys:12, Site area: 6157.3, Building pixels: 1443, Mask pixels: 4766, Generated GPR: 3.6332354175409147
Image: 1180.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:15, Site area: 19547.0, Building pixels: 3746, Mask pixels: 14134, Generated GPR: 3.9755200226404415
Image: 1379.png, GPR: 1.4, Simplified GPR: 1.4, Storeys:5, Site area: 17455.9, Building pixels: 5234, Mask pixels: 12042, Generated GPR: 2.173227038697891
Image: 145.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:15, Site area: 22094.4, Building pixels: 3814, Mask pixels: 16092, Generated GPR: 3.5551826994780016
Image: 1484.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:17, Site area: 10097.1, Building pixels: 2164, Mask pixels: 7503, Generated GPR: 4.903105424496868
Image: 1602.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:17, Site area: 13564.8, Building pixels: 3383, Mask pixels: 9811, Generated GPR: 5.86188971562532
Image: 1655.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:18, Site area: 27418.2, Building pixels: 4820, Mask pixels: 21801, Generated GPR: 3.9796339617448737
Image: 1670.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:13, Site area: 17940.2, Building pixels: 3071, Mask pixels: 11661, Generated GPR: 3.4236343366778152
Image: 1796.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:17, Site area: 13877.2, Building pixels: 1838, Mask pixels: 9220, Generated GPR: 3.3889370932754885
Image: 1811.png, GPR: 1.4, Simplified GPR: 1.4, Storeys:5, Site area: 7255.7, Building pixels: 2661, Mask pixels: 5084, Generated GPR: 2.617033831628639
Image: 1876.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:19, Site area: 10502.8, Building pixels: 2048, Mask pixels: 8279, Generated GPR: 4.700084551274308
Image: 191.png, GPR: 3.5, Simplified GPR: 3.0, Storeys:18, Site area: 13000.3, Building pixels: 2666, Mask pixels: 9066, Generated GPR: 5.29318332230311
Image: 2000.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:17, Site area: 13241.8, Building pixels: 2846, Mask pixels: 9503, Generated GPR: 5.091234347048301
Image: 434.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:16, Site area: 39401.6, Building pixels: 5843, Mask pixels: 28712, Generated GPR: 3.256060183895235
Image: 489.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:15, Site area: 28692.65, Building pixels: 3991, Mask pixels: 20518, Generated GPR: 2.91768203528609
Image: 491.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:16, Site area: 18747.8, Building pixels: 3279, Mask pixels: 12878, Generated GPR: 4.073924522441373
Image: 568.png, GPR: 3.4, Simplified GPR: 3.0, Storeys:19, Site area: 14344.0, Building pixels: 3037, Mask pixels: 10352, Generated GPR: 5.574091962905718 Accuracies:[0.27866788125602837, 0.14754820936639118, 2.203221704111912, 1.2707721359630715, 0.32517334088014715, 0.5523050276413507, 0.2697081069564292, 0.6343684748322893, 0.9539632385417732, 0.8950637913070827, 0.22272654881350554, 0.2103346761698174, 0.8693098797347423, 1.2381355006068133, 0.512338092086603, 0.6970781156827671, 0.5505048494739214, 0.389372397755281, 0.3579748408137909, 0.6394388126193289] Square error:[0.1522053445656989, 0.13606546304517753, 38.05681727936321, 4.1340462631427775, 0.9516393145724075, 0.5978800533735098, 0.570300909590882, 3.621810256749404, 8.190412744401973, 3.533023830144729, 0.38891978588357884, 0.3468468998357817, 1.4811713473286867, 6.760439673775321, 3.2155064273860208, 4.373261094274535, 1.3364751487878848, 0.6686039108296027, 1.1533138799009304, 4.726675863171238] Absolute error:[0.3901350337584397, 0.3688705234159779, 6.169020771513353, 2.0332354175409146, 0.9755200226404415, 0.773227038697891, 0.7551826994780018, 1.903105424496868, 2.8618897156253196, 1.8796339617448736, 0.6236343366778154, 0.5889370932754887, 1.217033831628639, 2.6000845512743083, 1.7931833223031104, 2.0912343470483012, 1.1560601838952351, 0.8176820352860901, 1.0739245224413727, 2.174091962905718] Accuracy:0.6609002812306523 MAE:1.6122843397824085 RMSE:2.0542080650474936